Accuracy Data: Valuable raw data generated by certain groups within an organization may need to be validated before they turn into normal or consistent content.
Interpretation of Data: Information obtained by one group may need to be mapped to a context that means standards for others in the organization.
Relevance Data: Quality and value of knowledge relies on relevance. Knowledge which has no relevance only adds complexity, cost and risk to the organization without compensation benefits. If the data does not support or actually answering the question being asked by the user, it requires the appropriate meta-data (data about data) which will be held in a knowledge management solution.
Ability of data to support / reject the hypothesis: Is the information really support decision-making? Does knowledge management solutions including statistical or rule-based model for workflow in which questions are asked?
Application of knowledge management solutions: Does organizational culture foster and support the voluntary use of knowledge management solutions?
Basic knowledge tends to be very complex and large: When knowledge of the database becomes very large and complex, placing the organization in a fix. Organizations can clear a very old file system, thus diluting their own knowledge management initiative. Or, could form another team to clean up the database files over, which increases costs substantially. In addition to this, the real challenge for an organization able to monitor the various departments and ensure that they are responsible for keeping them clean excessive file repository.